Change Data Capture (CDC) in Data Engineering: Concepts, Tools, and Real-World Implementation Strategies

Introduction

In today’s fast-paced data landscape, organizations need real-time insights to stay competitive. Change Data Capture (CDC) is a cornerstone of modern data engineering, enabling systems to track and propagate database changes—inserts, updates, and deletes—to downstream applications with minimal latency. Unlike batch processing, which relies on periodic data dumps, CDC streams changes as they occur, supporting use cases like real-time analytics, microservices synchronization, and cloud migrations.

According to Confluent, CDC "tracks all changes in data sources so they can be captured in destination systems, ensuring data integrity and consistency across multiple systems and environments." This is critical for scenarios like replicating operational data to a data warehouse without overloading the source database. Debezium, an open-source CDC platform, defines it as a distributed service that captures row-level changes and streams them as events to consumers, making it ideal for event-driven architectures.

This article dives into CDC concepts, explores tools like Debezium and Kafka, and walks through a real-world implementation for a crypto time-series data pipeline using Docker, PostgreSQL, Kafka, and Cassandra. We’ll also address common challenges—schema evolution, event ordering, late data, and fault tolerance—with practical solutions, drawing from official documentation and real-world configurations. By the end, you’ll have a clear blueprint for building robust CDC pipelines.

Concepts of CDC

CDC transforms database transactions into event streams, allowing applications to react to changes in near real-time. It’s particularly valuable for synchronizing data across heterogeneous systems, such as replicating a transactional database to a scalable NoSQL store for analytics.

Key Methods of CDC

CDC can be implemented through several approaches, each with distinct trade-offs:

1. Log-Based CDC: The most efficient method, it reads the database’s transaction log (e.g., PostgreSQL’s WAL or MySQL’s binlog) to capture changes. Logs record all operations sequentially, enabling low-latency capture with minimal impact on the source database. Debezium’s documentation highlights its effectiveness for capturing all operations, including deletes, without additional queries.

2. Trigger-Based CDC: Triggers are set on database tables to log changes into an "outbox" table or notify consumers directly. While simple, this adds overhead, as triggers execute SQL for each change. Confluent notes that triggers can impact write performance, making them less ideal for high-throughput systems.

3. Query-Based CDC: This involves polling the database for changes using timestamps or version columns. It’s straightforward but risks missing events and struggles with deletes. Redpanda recommends it only when log-based options are unavailable due to its inefficiencies.

CDC Architecture

A typical CDC pipeline includes:

• Source Database: Where changes occur (e.g., PostgreSQL).
• Capture Mechanism: A tool like Debezium parses logs or triggers to extract events.
• Event Stream: Apache Kafka buffers and distributes events reliably.
• Consumers: Downstream systems (e.g., Cassandra, data warehouses) process events.

The flow is: Changes are logged → CDC tool captures events → Events are streamed to Kafka → Consumers apply changes. For example, in a crypto pipeline, trade data is inserted into PostgreSQL, captured by Debezium, streamed via Kafka, and stored in Cassandra for scalable analytics.

Here’s a simplified architecture description:

[PostgreSQL] → [Transaction Log (WAL)] → [Debezium] → [Kafka Topics] → [Cassandra]

Tools for CDC

Several tools enable CDC, with Debezium and Kafka Connect standing out for their open-source flexibility and integration.

Debezium

Debezium is an open-source platform for log-based CDC, designed to work with Kafka. It supports connectors for databases like PostgreSQL, MySQL, and MongoDB, capturing row-level changes as events. Debezium performs an initial snapshot of the database and then streams ongoing changes, ensuring consistency and scalability.

Kafka Connect

Kafka Connect is a framework for integrating Kafka with external systems. It uses source connectors (e.g., Debezium) to capture data and sink connectors to write to targets like Cassandra. Confluent’s CDC guide emphasizes its role in simplifying CDC pipelines with managed connectors.

Real-World Implementation: Crypto Data Pipeline

To illustrate CDC, we’ll implement a pipeline for crypto time-series data, replicating trades from PostgreSQL to Cassandra via Kafka using Debezium. The setup uses Docker on an Ubuntu server, based on a real-world configuration shared by a data engineering team.

Prerequisites

• Ubuntu server (e.g., 22.04 LTS).
• Docker and Docker Compose installed.
• 10GB disk for PostgreSQL, 20GB for Cassandra, 2GB for Kafka/Zookeeper.
• Firewall allowing ports 5433, 2181, 9092, 8083, and 9042.

Step 1: Set Up the Ubuntu Server

Update the system and install Docker:

sudo apt update && sudo apt upgrade -y
sudo apt install docker.io docker-compose -y
sudo systemctl start docker
sudo systemctl enable docker
sudo usermod -aG docker $USER

Step 2: Configure Docker Services

The following docker-compose.yml orchestrates the pipeline:

version: '3.8'
services:
  postgres:
    image: debezium/postgres:16
    container_name: mydb
    environment:
      POSTGRES_USER: joy
      POSTGRES_PASSWORD: your password
      POSTGRES_DB: mydb
    command:
      - "postgres"
      - "-c"
      - "wal_level=logical"
      - "-c"
      - "max_wal_senders=1"
      - "-c"
      - "max_replication_slots=1"
    ports:
      - "5433:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data
  app:
    image: binance
    container_name: binance_app
    env_file: .env
    depends_on:
      - postgres
  zookeeper:
    image: confluentinc/cp-zookeeper:7.6.1
    container_name: zookeeper
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
    ports:
      - "2181:2181"
  kafka:
    image: confluentinc/cp-kafka:7.6.1
    container_name: kafka
    depends_on:
      - zookeeper
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
    ports:
      - "9092:9092"
  connect:
    image: debezium/connect:2.7.3.Final
    container_name: connect
    depends_on:
      - kafka
      - postgres
      - cassandra
    environment:
      BOOTSTRAP_SERVERS: kafka:9092
      GROUP_ID: 1
      CONFIG_STORAGE_TOPIC: connect-configs
      OFFSET_STORAGE_TOPIC: connect-offsets
      STATUS_STORAGE_TOPIC: connect-status
      CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 1
      CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 1
      KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverter
      CONNECT_PLUGIN_PATH: /kafka/connect,/kafka/connect/cassandra-sink,/kafka/connect/debezium-connector-postgres
      CONNECT_REPLICATION_FACTOR: 1
      CONNECT_OFFSET_FLUSH_INTERVAL_MS: 10000
    ports:
      - "8083:8083"
    volumes:
      - ./plugins:/kafka/connect
  cassandra:
    image: cassandra:5
    container_name: cassandra
    environment:
      - MAX_HEAP_SIZE=1G
      - HEAP_NEWSIZE=256M
    ports:
      - "9042:9042"
    volumes:
      - cassandra_data:/var/lib/cassandra
volumes:
  postgres_data:
  cassandra_data:

Explanation:

• postgres: Uses debezium/postgres:16 with CDC enabled via wal_level=logical. The mydb database is created with user joy and password your password. Port 5433 (host) maps to 5432 (container).
• app: A custom binance image (assumed to ingest crypto data into PostgreSQL tables like klines, trades).
• zookeeper and kafka: Provide the event streaming backbone, with Kafka advertising on port 9092.
• connect: Runs Debezium Connect to manage CDC connectors, exposing port 8083 for REST API.
• cassandra: Runs Cassandra 5 for scalable storage, with port 9042 for CQL access.
• restart: always: Ensures services run persistently, replacing the need for nohup.

Save this in ~/crypto-pipeline/docker-compose.yml and run:

cd ~/crypto-pipeline
docker-compose up -d

Step 3: Configure Debezium Connectors

PostgreSQL Source Connector (postgres-source.json)

{
  "name": "postgres-source",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "mydb",
    "database.port": "5432",
    "database.user": "joy",
    "database.password": "your password",
    "database.dbname": "mydb",
    "plugin.name": "pgoutput",
    "slot.name": "debezium_slot",
    "publication.name": "debezium_pub",
    "table.include.list": "public.klines,public.order_book,public.prices,public.ticker_24hr,public.trades",
    "topic.prefix": "dbz"
  }
}

Explanation:

• Captures changes from PostgreSQL tables (klines, order_book, prices, ticker_24hr, trades) and publishes to Kafka topics (e.g., dbz.klines).
• Uses pgoutput for logical decoding, with a dedicated replication slot (debezium_slot) and publication (debezium_pub).
• Connects to the mydb database with user joy and password your password.

Register the connector:

curl -X POST -H "Content-Type: application/json" --data @postgres-source.json http://localhost:8083/connectors

Cassandra Sink Connector (cassandra-sink.json)

{
  "name": "cassandra-sink",
  "config": {
    "connector.class": "com.datastax.oss.kafka.sink.CassandraSinkConnector",
    "tasks.max": "1",
    "topics": "dbz.public.prices,dbz.public.klines,dbz.public.order_book,dbz.public.ticker_24hr,dbz.public.trades",
    "contactPoints": "cassandra",
    "loadBalancing.localDc": "datacenter1",
    "topic.dbz.public.prices.crypto.prices.mapping": "symbol=value.symbol, price=value.price, event_time=value.event_time",
    "topic.dbz.public.klines.crypto.klines.mapping": "symbol=value.symbol, open_time=value.open_time, close_price=value.close_price, close_time=value.close_time, event_time=value.event_time, high_price=value.highPrice, low_price=value.lowPrice, open_price=value.openPrice, volume=value.volume",
    "topic.dbz.public.order_book.crypto.order_book.mapping": "symbol=value.symbol, event_time=value.event_time, side=value.side, price=value.price, qty=value.qty",
    "topic.dbz.public.ticker_24hr.crypto.ticker_24hr.mapping": "symbol=value.symbol, event_time=value.event_time, high_price=value.highPrice, last_price=value.lastPrice, low_price=value.lowPrice, price_change_percent=value.priceChangePercent, volume=value.volume",
    "topic.dbz.public.trades.crypto.trades.mapping": "id=value.id, price=value.price, qty=value.qty, quoteqty=value.quoteQty, time=value.time, isbuyermaker=value.isBuyerMaker, isbestmatch=value.isBestMatch, event_time=value.event_time",
    "transforms": "unwrap",
    "transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState"
  }
}

Explanation:

• Writes data from Kafka topics (dbz.klines, etc.) to Cassandra tables in the crypto keyspace.
• Connects to the cassandra service, ensuring scalable storage for crypto data.

Register the connector:

curl -X POST -H "Content-Type: application/json" --data @cassandra-sink.json http://localhost:8083/connectors

Step 4: Test the Pipeline

1. Populate PostgreSQL: Connect to the database:

   docker exec -it mydb psql -U joy -d mydb

Create a table and insert data:

   CREATE TABLE public.trades (id SERIAL PRIMARY KEY, symbol VARCHAR(10), price DECIMAL, timestamp TIMESTAMP);
   ALTER TABLE public.trades REPLICA IDENTITY FULL;
   INSERT INTO public.trades (symbol, price, timestamp) VALUES ('BTCUSDT', 50000.00, '2025-09-13 11:35:00');
   SELECT * FROM public.trades;

Exit with \q.

1. Verify Cassandra: Check replicated data:

   docker exec -it cassandra cqlsh

Query the trades table:

   DESCRIBE KEYSPACE crypto;
   SELECT * FROM crypto.trades;

Exit with exit.

The binance app inserts data into PostgreSQL, Debezium captures changes, Kafka streams them, and the sink connector writes to Cassandra.

Alternatively, you can automate the python script that fetches data from binance at a defined interval say 5mins using the BlockingScheduler library.

Challenges & Solutions

Building robust CDC pipelines involves addressing several challenges.

Schema Evolution

Challenge: Schema changes (e.g., adding/dropping columns) can break consumers. Decodable notes that forward-compatible changes (adding optional columns) allow old consumers to ignore new fields, while backward-compatible changes (dropping optional columns) ensure new consumers handle old data.

Solutions:

• Use a schema registry (e.g., Confluent Schema Registry) to enforce compatibility.

Event Ordering

Challenge: Incorrect event order can lead to inconsistencies, especially across distributed systems so maintaining the original transactional order matters.

Solutions:

• Kafka guarantees order within partitions. Use key-based partitioning (e.g., by symbol in the crypto pipeline) to ensure related events stay ordered.
• Debezium groups events by transaction for consistency. OLake recommends idempotent consumers to handle occasional out-of-order events.

Late Data

Challenge: Late-arriving events can disrupt aggregates or state in real-time analytics.

Solutions:

• Use watermarking in stream processors like Apache Flink to define lateness thresholds and design sinks to be idempotent(applying the same event twice has the same effect as applying it once) and able to accept corrections (e.g., update rows with newer timestamps).
• Buffer events in Kafka for replay. Confluent advocates event-time processing to handle delays accurately.

Fault Tolerance

Challenge: Failures in connectors or networks can cause data loss or duplicates.

Solutions:

• Debezium provides at-least-once delivery; use idempotent sinks (e.g., Cassandra’s upsert) to deduplicate.
• Kafka’s replication ensures durability. Configure high availability for PostgreSQL to prevent replication slot buildup. OLake suggests monitoring with Prometheus for proactive fault detection.

Conclusion

CDC is a game-changer for real-time data engineering, enabling seamless synchronization across systems. Using Debezium, Kafka, and connectors, our crypto pipeline demonstrates how to replicate data from PostgreSQL to Cassandra efficiently. By addressing schema evolution, ordering, late data, and fault tolerance, engineers can build reliable pipelines. As data demands grow, CDC will remain a critical tool for agile, data-driven organizations.

References:

• Confluent: Change Data Capture
• Debezium Documentation
• Redpanda: CDC Guide
• Microsoft: About CDC
• Decodable: CDC Explained
• OLake: CDC Insights